The extract, transform, and load (ETL) process is at the core of data warehousing
architectures. As such, the success of data warehouse (DW) projects is essentially based on …

In malicious URLs detection, traditional classifiers are challenged because the data volume
is huge, patterns are changing over time, and the correlations among features are …

Since the late 1970s, relational database technology has been adopted by most
organizations to store their essential data. However, nowadays, the needs of these …

The dynamicity of RDF data has motivated the development of solutions for archiving, ie, the
task of storing and querying previous versions of an RDF dataset. Querying the history of a …

Abstract The use of Semantic Web and linked data increases the possibility of data
accessibility, interpretability, and interoperability. It supports cross‐domain data and …

Many clinical trials and scientific studies have been conducted aiming for better
understanding of specific medical conditions. However, these studies are often based on a …

Organization with considerable investment into data warehousing, the influx of various data
types and forms requires certain ways of prepping data and staging platform that support …

Big data and geographic information systems (GIS) are two technologies that have
increasingly influenced many areas in the last 10 years and will continue to improve and …

Data analytics plays a vital role in contemporary organizations, through analytics,
organizations are able to derive knowledge and intelligence from data to support strategic …

Exploratory OLAP aims at coupling the precision and detail of corporate data with the
information wealth of LOD. While some techniques to create, publish, and query RDF cubes …